Challenges in defining Long COVID: Striking differences across literature, Electronic Health Records, and patient-reported information

  1. Halie M. Rando
  2. Tellen D. Bennett
  3. James Brian Byrd
  4. Carolyn Bramante
  5. Tiffany J. Callahan
  6. Christopher G. Chute
  7. Hannah E. Davis
  8. Rachel Deer
  9. Joel Gagnier
  10. Farrukh M Koraishy
  11. Feifan Liu
  12. Julie A. McMurry
  13. Richard A. Moffitt
  14. Emily R. Pfaff
  15. Justin T. Reese
  16. Rose Relevo
  17. Peter N. Robinson
  18. Joel H. Saltz
  19. Anthony Solomonides
  20. Anupam Sule
  21. Umit Topaloglu
  22. Melissa A. Haendel

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Abstract

Since late 2019, the novel coronavirus SARS-CoV-2 has introduced a wide array of health challenges globally. In addition to a complex acute presentation that can affect multiple organ systems, increasing evidence points to long-term sequelae being common and impactful. The worldwide scientific community is forging ahead to characterize a wide range of outcomes associated with SARS-CoV-2 infection; however the underlying assumptions in these studies have varied so widely that the resulting data are difficult to compareFormal definitions are needed in order to design robust and consistent studies of Long COVID that consistently capture variation in long-term outcomes. Even the condition itself goes by three terms, most widely “Long COVID”, but also “COVID-19 syndrome (PACS)” or, “post-acute sequelae of SARS-CoV-2 infection (PASC)”. In the present study, we investigate the definitions used in the literature published to date and compare them against data available from electronic health records and patient-reported information collected via surveys. Long COVID holds the potential to produce a second public health crisis on the heels of the pandemic itself. Proactive efforts to identify the characteristics of this heterogeneous condition are imperative for a rigorous scientific effort to investigate and mitigate this threat.

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  1. ScreenIT

    SciScore for 10.1101/2021.03.20.21253896: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    In addition to searching PubMed (MEDLINE), we included searches of specialized databases (e.g.,CoronaCentral; WHO Global Literature on Coronavirus Disease) and relied on expert recommended key articles, with snowball techniques to find similar studies.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    We then used a Python script to extract symptoms;.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.03.20.21253896 on medRxiv